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21. 发明申请

US20110072023A1 Detect, Index, and Retrieve Term-Group Attributes for Network Search 审中-公开
标题翻译：检测，索引和检索网络搜索的术语组属性
公开(公告)号：US20110072023A1
公开(公告)日：2011-03-24
申请号：US12563347
申请日：2009-09-21
申请人： Yumao Lu
发明人： Yumao Lu
IPC分类号： G06F17/30
CPC分类号： G06F16/951 , G06F16/313 , G06F16/353
摘要： In one embodiment, concept tag a network document comprising document words based on a set of document concepts, each of the document words being indexed with its position within the network document, such that for each of the document words, if the document word represents one of the document concepts, index a document concept tag corresponding to the one document concept with the position of the document word within the network document. Concept tag a search query based on a set of query concepts by associating appropriate query concept tags with selected query words. For each of the query words associated with the query concept tags, determine zero or more first positions within the network document at which the document words match the query word or its synonym and zero or more second positions within the network document at which the document concept tags correspond to the query concept tag.
摘要翻译：在一个实施例中，概念标记包括基于一组文档概念的文档单词的网络文档，每个文档单词以其在网络文档内的位置被索引，使得对于每个文档单词，如果文档单词表示一个的文档概念，将与文档概念相对应的文档概念标签与网络文档中的文档字的位置进行索引。概念通过将适当的查询概念标签与选定的查询字相关联，基于一组查询概念来标记搜索查询。对于与查询概念标签相关联的每个查询词，确定网络文档中的零个或多个第一位置，在该文档中文档字符与查询词或其同义词匹配，并且在网络文档内的零个或更多个第二位置处，文档概念标签对应于查询概念标签。

22. 发明申请

US20110040769A1 Query-URL N-Gram Features in Web Ranking 审中-公开
标题翻译： Web排名中的查询 - URL N-gram特征
公开(公告)号：US20110040769A1
公开(公告)日：2011-02-17
申请号：US12541063
申请日：2009-08-13
申请人： Huihsin Tseng , Longbin Chen , Yumao Lu , Fachun Peng
发明人： Huihsin Tseng , Longbin Chen , Yumao Lu , Fachun Peng
IPC分类号： G06F17/30
CPC分类号： G06F16/951
摘要： In one embodiment, access one or more pairs of search query and clicked Uniform Resource Locator (URL). For each of the pairs of search query and clicked URL, segment the search query into one or more query segments and the clicked URL into one or more URL segments; construct one or more query-URL n-grams, each of which comprises a query part comprising at least one of the query segments and a URL part comprising at least one of the URL segments; and calculate one or more association scores, each of which for one of the query-URL n-grams and represents a similarity between the query part and the URL part of the query-URL n-gram and is based on a first frequency of the query part and the URL part, a second frequency of the query part, and a third frequency of the URL part.
摘要翻译：在一个实施例中，访问一对或多对搜索查询和点击的统一资源定位符（URL）。对于每一对搜索查询和点击的URL，将搜索查询分割成一个或多个查询段，并将点击的URL分段成一个或多个URL段; 构造一个或多个查询URL n克，每个查询URL n-gram包括包括至少一个查询段的查询部分和包括至少一个URL段的URL部分; 并且计算一个或多个关联分数，其中每个关联分数中的每一个用于查询URL n-gram中的一个，并且表示查询部分与查询URL n-gram的URL部分之间的相似度，并且基于第一频率查询部分和URL部分，查询部分的第二个频率，以及URL部分的第三个频率。

23. 发明申请

US20110072021A1 Semantic and Text Matching Techniques for Network Search 有权
标题翻译：网络搜索的语义和文本匹配技术
公开(公告)号：US20110072021A1
公开(公告)日：2011-03-24
申请号：US12563357
申请日：2009-09-21
申请人： Yumao Lu , Lei Duan , Fan Li , Benoit Dumoulin , Xing Wei
发明人： Yumao Lu , Lei Duan , Fan Li , Benoit Dumoulin , Xing Wei
IPC分类号： G06F17/30
CPC分类号： G06F17/30864
摘要： In one embodiment, access a search query comprising one or more query words, at least one of the query words representing one or more query concepts; access a network document identified for a search query by a search engine, the network document comprising one or more document words, at least one of the document words representing one or more document concepts; semantic-text match the search query and the network document to determine one or more negative semantic-text matches; and construct one or more negative features based on the negative semantic-text matches.
摘要翻译：在一个实施例中，访问包括一个或多个查询词的搜索查询，表示一个或多个查询概念的查询词中的至少一个; 访问由搜索引擎识别为搜索查询的网络文档，所述网络文档包括一个或多个文档字，所述文档字中的至少一个表示一个或多个文档概念; 语义文本匹配搜索查询和网络文档以确定一个或多个否定语义文本匹配; 并基于负面语义文本匹配构造一个或多个负面特征。

24. 发明申请

US20100191740A1 SYSTEM AND METHOD FOR RANKING WEB SEARCHES WITH QUANTIFIED SEMANTIC FEATURES 审中-公开
标题翻译：使用量化的语义特征排序网页搜索的系统和方法
公开(公告)号：US20100191740A1
公开(公告)日：2010-07-29
申请号：US12360016
申请日：2009-01-26
申请人： Yumao Lu , Benoit Dumoulin
发明人： Yumao Lu , Benoit Dumoulin
IPC分类号： G06F17/30
CPC分类号： G06F16/9535
摘要： A system and method for ranking web searches with quantified semantic features. A query for a web search is received from a user. The query is segmented and tagged into one or more linguistic segments using linguistic analysis. At least some of the linguistic segments are tagged with a linguistic type. A query execution plan is generated comprising the linguistic segments and, for each of the linguistic segments tagged with a linguistic type, at least one tag attribute comprising at least one domain specific feature of the linguistic type. A search is performed for documents matching the query. Each of the documents is scored for each of the linguistic segments of the query execution plan using the tag attributes of the respective linguistic segment. The documents are ranked using a function that uses the scores of the documents. A ranked list of the documents is transmitted back to the user.
摘要翻译：一种用量化语义特征对网页搜索进行排名的系统和方法。从用户接收到对网页搜索的查询。使用语言分析将查询分段并标记为一个或多个语言段。至少一些语言段被用语言类型标记。生成包括语言段的查询执行计划，并且对于每个具有语言类型的语言段，至少包括语言类型的至少一个域特定特征的标签属性。对与查询匹配的文档执行搜索。使用相应语言段的标签属性对查询执行计划中的每个语言段进行每个文档的评分。使用使用文档分数的函数对文档进行排名。将文档的排名列表传回给用户。

25. 发明申请

US20100185623A1 TOPICAL RANKING IN INFORMATION RETRIEVAL 审中-公开
标题翻译：信息检索中的主题排名
公开(公告)号：US20100185623A1
公开(公告)日：2010-07-22
申请号：US12354533
申请日：2009-01-15
申请人： Yumao Lu , Benoit Dumoulin
发明人： Yumao Lu , Benoit Dumoulin
IPC分类号： G06F17/30
CPC分类号： G06F16/951 , G06F16/334
摘要： An aggregate ranking model is generated, which comprises a general ranking model and one or more topical training models. Each topical ranking model is associated with a topic, or topic class, and for use in ranking search result items determined to belong to the topic, or topic class. As one example, the topical ranking model is trained using a set of topical training data, e.g., training data determined to belong to the topic, or topic class, a general ranking model and a residue, or error, determined from a general ranking generated by the general ranking model for the topical training data, with the topical ranking model being trained to minimize the general ranking model's error in the aggregate ranking model.
摘要翻译：产生一个综合排名模型，其中包括一般排名模型和一个或多个主题训练模型。每个主题排名模型与主题或主题类相关联，并且用于对确定属于主题或主题类的搜索结果项进行排名。作为一个示例，使用一组主题训练数据训练主题排名模型，例如，确定为属于主题的训练数据，或主题类别，一般排名模型和残差或错误，其从生成的一般排名确定通过主题训练数据的一般排名模型，对主题排名模型进行训练，以最小化总排名模型在总体排名模型中的误差。

26. 发明申请

US20100114878A1 SELECTIVE TERM WEIGHTING FOR WEB SEARCH BASED ON AUTOMATIC SEMANTIC PARSING 审中-公开
标题翻译：基于自动语义分析的网络搜索选择性加权
公开(公告)号：US20100114878A1
公开(公告)日：2010-05-06
申请号：US12256371
申请日：2008-10-22
申请人： Yumao Lu , Benoit Dumoulin
发明人： Yumao Lu , Benoit Dumoulin
IPC分类号： G06F17/30 , G06F7/00
CPC分类号： G06F16/334
摘要： A method is provided for selecting relevant documents returned from a search query. When a search engine finds search terms in documents, the document score is based on the frequency of the occurrence of those terms, the category of the term, and the section of the document in which the term is found. Each (category type, document section) pair is assigned a weight that is used to modify the contribution of term frequency. The weights are determined in an offline process using historical data and human validation. Through this empirical process, the weight assignments are made to correlate high relevance scores with documents that humans would find relevant to a search query.
摘要翻译：提供了一种用于选择从搜索查询返回的相关文档的方法。当搜索引擎在文档中找到搜索词时，文档分数基于这些术语的发生频率，术语的类别以及找到该术语的文档的部分。每个（类别类型，文档部分）对被分配一个权重，用于修改术语频率的贡献。权重是使用历史数据和人类验证在离线过程中确定的。通过这个经验过程，进行权重分配以将高相关性分数与人类将会发现与搜索查询相关的文档相关联。

27. 发明申请

US20100094835A1 AUTOMATIC QUERY CONCEPTS IDENTIFICATION AND DRIFTING FOR WEB SEARCH 审中-公开
标题翻译：自动查询概念识别和网络搜索
公开(公告)号：US20100094835A1
公开(公告)日：2010-04-15
申请号：US12252220
申请日：2008-10-15
申请人： Yumao Lu , Benoit Dumoulin
发明人： Yumao Lu , Benoit Dumoulin
IPC分类号： G06F7/06 , G06F17/30 , G06N5/02
CPC分类号： G06F16/951 , G06F16/3338 , G06F16/374
摘要： Techniques are described for automatically determining which terms in a search query may be augmented by contextually similar terms such that more relevant results can be displayed to a user. Contextually similar words are determined based on training data, including a web corpus and a query log. Once contextually similar words are determined, they may be inserted into a search query and used to find more relevant results. Consequently, documents that contain helpful information but may not have exact word matches may be found more readily by a search engine.
摘要翻译：描述了用于自动确定搜索查询中哪些术语可以通过上下文相似术语来增强的技术，使得可以向用户显示更相关的结果。基于训练数据确定上下文相似的词，包括网络语料库和查询日志。一旦上下文相似的词被确定，它们可以被插入到搜索查询中并用于找到更相关的结果。因此，搜索引擎可以更容易地找到包含有用信息但可能没有确切字词匹配的文档。

28. 发明授权

US07555152B2 System and method for detecting ground glass nodules in medical images 有权
标题翻译：用于检测医学图像中玻璃结节的系统和方法
公开(公告)号：US07555152B2
公开(公告)日：2009-06-30
申请号：US11324503
申请日：2006-01-03
申请人： Lin Hong , Yumao Lu , Hong Shen
发明人： Lin Hong , Yumao Lu , Hong Shen
IPC分类号： G06K9/00 , A61K39/00
CPC分类号： G06T7/0012 , G06T7/11 , G06T7/143 , G06T2207/10081 , G06T2207/30064
摘要： Detecting ground glass nodules in medical images includes calculating a probability distribution function of background lung tissue in a chest image, estimating a variation range of the background tissue probability distribution function, estimating a probability distribution function of an image point belonging to a ground glass nodule from the variation range of the background tissue probability distribution function by using a function corresponding to the variation range of the background tissue probability distribution function, and calculating a log likelihood function of the image from the background tissue probability distribution function and the estimated ground glass nodule probability distribution function, wherein the log likelihood function represents the confidence that a point in the image is not part of a ground glass nodule. The log likelihood function value for each point is compared to a confidence value of the background tissue, to determine if the point is a candidate ground glass nodule location.
摘要翻译：在医学图像中检测磨玻璃结节包括计算胸部图像中背景肺组织的概率分布函数，估计背景组织概率分布函数的变化范围，估计属于研磨玻璃结节的图像点的概率分布函数，通过使用与背景组织概率分布函数的变化范围相对应的函数来计算背景组织概率分布函数的变化范围，以及从背景组织概率分布函数和估计的研磨玻璃结节概率计算图像的对数似然函数分布函数，其中对数似然函数表示图像中的点不是研磨玻璃结节的一部分的置信度。将每个点的对数似然函数值与背景组织的置信度值进行比较，以确定该点是否为候选研磨玻璃结节位置。

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